1. Field of the Invention
The present invention relates to method and apparatus for object detection in an image. More specifically, the present invention relates to method and apparatus for detecting a plurality of object regions with similar specific structural features in an image.
2. Description of the Related Art
In recent years, object detection is popularly applied in the field of image processing, computer vision and pattern recognition and plays an important role therein. A common kind of object detection is detection for objects with similar and even the same features in an image, such as human pupils, etc, and there exists many type of techniques for such object detection.
Hereinafter, we would take pupil detection in a face image as an example to explain the current techniques for detecting a plurality of objects with similar and even the same features in an image in the prior art.
For pupil detection, since the pupil center is similar to the iris center and the shape of eye iris is approximately circular, the iris boundary is actually detected and used to estimate the pupil center. The best known and thoroughly examined algorithm is perhaps the algorithm based on the work described in J. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence”, PAMI, 1993 (hereinafter to be referred as Daugman). The technique uses an integro-differential operator to find the circular boundary of an iris. Another well-known algorithm is based on the circular Hough transform employed by R. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proc. IEEE, 1997.
However, the general methods mainly try their best to precisely localize the pupil center in a single eye image, that is, separately determine respective pupil centers in respective eyes, and do not pay much attention on the relation between the left eye and the right eye in a face image. Therefore, the results gotten by the general methods show the difference of the radius sizes of two pupils is very large, as shown in FIG. 13A.
Furthermore, another shortcoming in the general methods is that, when the uncertainty (e.g. uneven light) of photo environment and the local circular-like dark areas of surrounding objects (e.g. eyebrows, eyeglasses, and hair) appear in the image, the detection result of the general methods becomes unreliable.
U.S. Pat. No. 7,197,166 discloses an iris extraction method capable of precisely determining positions and sizes of irises in a digital face image. The method uses the relation between the left eye and the right eye in the face image to localize the iris, and as shown in FIG. 2 which shows the key flow chart of the method, the method comprises the following steps: roughly detecting the positions of two eyes in a face image, and measuring the distance between the two positions; defining two rectangular searching regions according to the distance (the scales of the two rectangles are related to the distance); and precisely localizing an iris for each of rectangular searching regions separately. Although the method employs the distance between the positions of two eyes to determine the rectangular searching regions of irises or pupils, the method does not consider the similar features (e.g. the same radius) of two irises. This might result in inaccuracy of the final iris location.
As describe above, there still needs a method capable of accurately detecting a plurality of object regions with similar features in an image.